Data representativeness problem in credit scoring

Josef Ditrich

Acta Oeconomica Pragensia, 2015, vol. 2015, issue 3, 3-17

Abstract: When building models, it is common to split the whole dataset into a development and a validation sample. In some cases, using random sampling instead of stratified sampling can lead to loss of representativeness of final samples. In such cases, a model built on these data gives different or unexpected results when its performance is measured on the validation sample. In the business area, a lack of representativeness can cause interpretative problems and can have a huge financial impact when a biased model is involved in the credit granting process. The aim of this paper is to examine and understand why representativeness should be checked before the start of modelling. The paper deals with methods of identification of selection bias in time. It recommends using three tests as a common part of the data preparation process.

Keywords: credit scoring; credit risk models; selection bias; random sampling; stratified sampling; data splitting (search for similar items in EconPapers)
JEL-codes: C18 C80 C83 (search for similar items in EconPapers)
Date: 2015
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DOI: 10.18267/j.aop.472

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